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        <title>KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters</title>
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        <namePart type="given">SungHo</namePart>
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    <abstract>The Korean writing system, Hangeul, has a unique character representation rigidly following the invention principles recorded in Hunminjeongeum. However, existing pre-trained language models (PLMs) for Korean have overlooked these principles. In this paper, we introduce a novel framework for Korean PLMs called KOMBO, which firstly brings the invention principles of Hangeul to represent character. Our proposed method, KOMBO, exhibits notable experimental proficiency across diverse NLP tasks. In particular, our method outperforms the state-of-the-art Korean PLM by an average of 2.11% in five Korean natural language understanding tasks. Furthermore, extensive experiments demonstrate that our proposed method is suitable for comprehending the linguistic features of the Korean language. Consequently, we shed light on the superiority of using subcharacters over the typical subword-based approach for Korean PLMs. Our code is available at: https://github.com/SungHo3268/KOMBO.</abstract>
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%0 Conference Proceedings
%T KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters
%A Kim, SungHo
%A Park, Juhyeong
%A Kim, Yeachan
%A Lee, SangKeun
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F kim-etal-2024-kombo
%X The Korean writing system, Hangeul, has a unique character representation rigidly following the invention principles recorded in Hunminjeongeum. However, existing pre-trained language models (PLMs) for Korean have overlooked these principles. In this paper, we introduce a novel framework for Korean PLMs called KOMBO, which firstly brings the invention principles of Hangeul to represent character. Our proposed method, KOMBO, exhibits notable experimental proficiency across diverse NLP tasks. In particular, our method outperforms the state-of-the-art Korean PLM by an average of 2.11% in five Korean natural language understanding tasks. Furthermore, extensive experiments demonstrate that our proposed method is suitable for comprehending the linguistic features of the Korean language. Consequently, we shed light on the superiority of using subcharacters over the typical subword-based approach for Korean PLMs. Our code is available at: https://github.com/SungHo3268/KOMBO.
%U https://aclanthology.org/2024.findings-acl.302
%P 5102-5119

Markdown (Informal)

[KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters](https://aclanthology.org/2024.findings-acl.302) (Kim et al., Findings 2024)

• KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters (Kim et al., Findings 2024)

ACL

• SungHo Kim, Juhyeong Park, Yeachan Kim, and SangKeun Lee. 2024. KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters. In Findings of the Association for Computational Linguistics ACL 2024, pages 5102–5119, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.